MiMo-V2.5-Pro vs Qwen3 VL 235B A22B Instruct
MiMo-V2.5-Pro and Qwen3 VL 235B A22B Instruct are closely matched at 25.6 and 24.6 on the LLM Stats Score. Qwen3 VL 235B A22B Instruct is 1.5x cheaper per token.
Xiaomi · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
MiMo-V2.5-Pro and Qwen3 VL 235B A22B Instruct are closely matched on the overall LLM Stats Score at 25.6 and 24.6.
The models split the 4 individual benchmarks reported for both models evenly.
On price, Qwen3 VL 235B A22B Instruct is roughly 1.5x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
MiMo-V2.5-Pro also accepts a larger context window (1,048,576 input tokens), making it the stronger choice for long documents and large codebases.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose MiMo-V2.5-Pro
- your work emphasizes agents — it leads those capability indexes
- you process long inputs — it offers a 1,048,576 token context window
- you want the most recent training data — it shipped Apr 2026
Choose Qwen3 VL 235B A22B Instruct
- cost matters — it's about 1.5x cheaper per token
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
31 reported for MiMo-V2.5-Pro · 50 for Qwen3 VL 235B A22B Instruct
MiMo-V2.5-Pro outperforms in 2 benchmarks (MMLU, MMLU-Redux), while Qwen3 VL 235B A22B Instruct is better at 2 benchmarks (LiveCodeBench v6, MMLU-Pro).
Both models are evenly matched across the benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, MiMo-V2.5-Pro ($0.43/1M tokens) is 2.2x more expensive than Qwen3 VL 235B A22B Instruct ($0.20/1M tokens).
For output processing, MiMo-V2.5-Pro ($0.87/1M tokens) is 1.0x cheaper than Qwen3 VL 235B A22B Instruct ($0.88/1M tokens).
In conclusion, MiMo-V2.5-Pro is more expensive than Qwen3 VL 235B A22B Instruct.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
MiMo-V2.5-Pro has 787.2B more parameters than Qwen3 VL 235B A22B Instruct, making it 333.6% larger.
Context Window
Maximum input and output token capacity
MiMo-V2.5-Pro accepts 1,048,576 input tokens compared to Qwen3 VL 235B A22B Instruct's 262,144 tokens. Qwen3 VL 235B A22B Instruct can generate longer responses up to 262,144 tokens, while MiMo-V2.5-Pro is limited to 131,072 tokens.
Input capabilities
Documented input modalities across available providers
Qwen3 VL 235B A22B Instruct supports multimodal inputs, whereas MiMo-V2.5-Pro does not.
Qwen3 VL 235B A22B Instruct can handle both text and other forms of data like images, making it suitable for multimodal applications.
MiMo-V2.5-Pro
Qwen3 VL 235B A22B Instruct
License
Usage and distribution terms
MiMo-V2.5-Pro is licensed under MIT, while Qwen3 VL 235B A22B Instruct uses Apache 2.0.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Apache 2.0
Open weights
Release Timeline
When each model was launched
MiMo-V2.5-Pro was released on 2026-04-27, while Qwen3 VL 235B A22B Instruct was released on 2025-09-22.
MiMo-V2.5-Pro is 7 months newer than Qwen3 VL 235B A22B Instruct.
Apr 27, 2026
5 months ago
7mo newerSep 22, 2025
1.0 years ago
Knowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
MiMo-V2.5-Pro is available from Xiaomi, DeepInfra, Novita. Qwen3 VL 235B A22B Instruct is available from DeepInfra, Novita.
MiMo-V2.5-Pro
Qwen3 VL 235B A22B Instruct
Outputs Comparison
Judge for yourself.
Run your own prompts against MiMo-V2.5-Pro and Qwen3 VL 235B A22B Instruct side-by-side, then vote on the output you prefer.
FAQ
Common questions about MiMo-V2.5-Pro vs Qwen3 VL 235B A22B Instruct.